code: def play_game(env, network, n_steps=1000):
      state = env.reset()
  
      for _ in range(n_steps):
          env.render()
  
          q = network.predict(state)
          action = int(np.argmax(q[0]))
  
          state, _, done, _ = env.step(action)
  
          if done:
              break
  
file_path: neupy/examples/reinforcement_learning/rl_cartpole.py
imports: import os
  import random
  import argparse
  from collections import deque
  import gym
  import numpy
  from neupy import layers, algorithms, utils, storage
label_desc: Use image node_id 2 for calling a global function with example usage: play_game(env, network, n_steps) without return types